Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ANIME305/Anime-GAN-tensorflow
https://github.com/ANLGBOY/MADE-Masked-Autoencoder-for-Distribution-Estimation-using-PyTorch
https://github.com/ANLGBOY/MADE-with-PyTorch
https://github.com/ANLGBOY/RealNVP-with-PyTorch
https://github.com/AaltoVision/pioneer
Pioneer Networks: Progressively Growing Generative Autoencoder
https://github.com/Abhaya1998/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/AbhinavJhanwar/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AbhinavTalari/Night2Day
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Abhis-123/TextToImage
https://github.com/Adi-iitd/AI-Art
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Adityasakare/Udacity-Deep-Learning-Nanodegree-
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AemikaChow/DATASOURCE
https://github.com/Afsaan/CNN-Architectures
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Aggrathon/MtGan
https://github.com/AhmedAmraniAkdi/BdgetNvidiaGaugan
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/AhmedAmraniAkdi/BudgetNvidiaGaugan
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/AhmedAmraniAkdi/SmallNvidiaGaugan
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/Aiwizo/template-nvae
https://github.com/Aiyoj/Thumbnail-Generation
User Constrained Thumbnail Generation using Adaptive Convolutions
https://github.com/Ajasra/TattooStyleGAN2
https://github.com/Ajasra/stylegan2
https://github.com/Ajasra/tattoo
https://github.com/Akash-Ramjyothi/Cloud-Removal-with-GAN-Satellite-Image-Processing
https://github.com/Akash-Ramjyothi/Cloud-and-Fog-removal-System-using-Generative-Adversarial-Networks-Satellite-Image-Processing
https://github.com/Akash-Ramjyothi/Cloud-and-Fog-removal-from-Satellite-Images-using-Generative-Adversarial-Netrowks-GANs-
https://github.com/AlexHex7/SimGAN_pytorch
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/AlexMGitHub/PokeGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AlexVerine/PrecisionRecallGan
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/Alexandre-Rio/ot_generative_models
https://github.com/AlexeiKaplenko/FaceShifter_mine
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/AlexiaJM/Deep-learning-with-cats
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/AlexiaJM/GANsBeyondDivergenceMin
The relativistic discriminator: a key element missing from standard GAN
https://github.com/Alighasemzadeh83/Pix2Pix-implementation-from-scratch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/AllenChen0958/homework3-GAN-Dissection
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
https://github.com/AlliBalliBaba/PixFace
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.